Analyzing acoustic emission data to identify cracking modes in cement paste using an artificial neural network

•A new analysis procedure was developed to analyze and interpret AE data.•AE data driven from a compression test on a cement paste were classified.•An artificial neural network model was trained to discriminate the AE data.•The trained neural network was applied to discriminate the AE signals.•Crack...

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Vydané v:Construction & building materials Ročník 267; číslo C; s. 121047
Hlavní autori: Soltangharaei, V., Anay, R., Assi, L., Bayat, M., Rose, J.R., Ziehl, P.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United Kingdom Elsevier Ltd 18.01.2021
Elsevier
Predmet:
Acoustic emission
Artificial neural network
Cement paste
Crack mechanism
Ray-tracing algorithm
Unsupervised pattern recognition
Crack mechanism
Acoustic emission
Artificial neural network
Cement paste
Ray-tracing algorithm
Unsupervised pattern recognition
ISSN:0950-0618, 1879-0526
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Popis
Shrnutí:•A new analysis procedure was developed to analyze and interpret AE data.•AE data driven from a compression test on a cement paste were classified.•An artificial neural network model was trained to discriminate the AE data.•The trained neural network was applied to discriminate the AE signals.•Cracking modes and mechanisms were identified. The focus of this research is the identification of cracking mechanisms for cement paste using acoustic emission data, recorded from compression and notched four-point bending tests. A procedure is developed for analyzing the data by employing an agglomerative hierarchical clustering method, an artificial neural network, and a ray-tracing source location algorithm. An agglomerative hierarchical clustering method is utilized to cluster the AE data from a compression test using frequency-dependent features. A neural network is trained using the compression test data and applied to the AE data emitted during the four-point bending test. The clustered data from the four-point bending test is localized using a ray-tracing algorithm. Based on the occurrence and locations of the clustered events and signal feature analyses, potential cracking mechanisms are identified and assigned.
Bibliografia:USDOE
ISSN:0950-0618
1879-0526
DOI:10.1016/j.conbuildmat.2020.121047